Data governance
Find the data first,then talk analytics
Most data programmes stall in the same place: nobody can say where a number came from, who depends on it, or whether it can be trusted. We begin with inventory and classification, then put the warehouse, lineage, quality and metrics onto a platform that runs.
Inventory first, tooling second
Inventory and classification first: systems, tables, interfaces and owners. The platform decision comes after. Projects that buy the tool first and look for the use case later tend to end at handover.
One platform for the warehouse and the governance
Modelling, lineage capture, quality rules and metric definitions are maintained together. A changed definition can be traced to everything downstream, so the dashboard and the model stop disagreeing.
Governance that feeds compliance
Classification results wire straight into access policy, masking rules and audit logs, so personal and critical data handling leaves an evidence trail regulators accept.
Data governance capability domains
Six domains from strategy through to steady-state operations — delivered on their own, or combined into a programme covering your core data domains.
Governance strategy & operating model
Maturity assessment against DCMM and DAMA-DMBOK, then a blueprint, an operating model with named owners and the metrics to hold it in place.
Standards, metadata & master data
Data standards, field-level lineage and a data map, plus master data identification, golden records and distribution — no more one term with two meanings.
Architecture, modelling & integration
Layered architecture and lakehouse selection, dimensional and normalised modelling, ETL / ELT and CDC pipelines, with definitions, scheduling and quality gates fixed in place.
Data quality & asset operations
Quality rules, profiling, and every issue tracked from detection to closure. Alongside the asset catalogue, packaged data services and controlled sharing that support data-as-asset reporting.
Data security & compliance
Classification and grading (DSMM), sensitive-data discovery and masking, least-privilege access and audit trails, covering PIPL, GDPR and cross-border transfer review.
Analytics & decision support
Metric systems and North Star design, BI dashboards, statistical modelling and forecasting, segmentation and attribution — governance that ends in business answers.
Frequently asked questions
Common questions on scope, effort and self-hosted deployment. If yours is not here, talk to the data team directly.
It depends on the symptom. If definitions keep conflicting, downstream teams hesitate to use the data, and tracing an error means asking around, what is missing is a catalogue, lineage and quality rules — another warehouse will not supply them. We assess first and keep whatever already works.
Start with an assessment, then talk scope
Security, data and AI each start with a review of where you stand. The report and its findings are yours, whether or not the engagement continues.